112 results about "Noise floor" patented technology

The invention discloses a multi-bandwidth spectrum data signal adaptive extraction method based on a signal-to-noise ratio threshold, and relates to the technical field of spectrum signal extraction. The frequency spectrum data collection covers 30MHz-6GHz, the sampling rate is 20MHz, 16-bit quantization is realized, and the hardware fluctuation is eliminated through the dual-channel design; according to noise floor estimation, 50 frames of no-signal samples are taken, mean value calculation is carried out through a sliding window, then median filtering is carried out, and the noise power and confidence interval of each frequency point are obtained; basic values are set for a signal-to-noise ratio threshold according to frequency bands, dynamic adjustment is performed in combination with real-time noise standard deviation, and compensation is added to a known frequency band; for multi-bandwidth detection, wavelet packet decomposition is adapted to 100kHz-5MHz, candidate signals are marked when energy detection exceeds a threshold, and a range is determined by morphological filtering; extracting parameters such as center frequency, analyzing time-frequency characteristics through short-time Fourier transform, and calculating 4-order cumulants to establish feature vectors; coherent accumulation times are adjusted according to a signal-to-noise ratio, energy is superposed through phase alignment, and a purified signal is output through band-pass filtering. According to the invention, the signal detection precision and adaptability in a complex environment are improved, and misjudgment is reduced.

The invention discloses a self-adaptive large dynamic detection noise floor suppression system and a control method thereof. The system comprises a filtering selection front-end module, a detection synthesis module, a frequency measurement receiving module and an FPGA feedback control module. The method is composed of four main parts, namely a filtering selection front end, detection synthesis, frequency measurement receiving and FPGA feedback control. Wherein the filtering selection front end and the frequency measurement receiving end receive two groups of external radio frequency signals at the same time. And frequency measurement is carried out through a mature single-bit receiving scheme in frequency measurement receiving, and a result is sent to the feedback control module, so that switching of a filtering channel and suppression of a noise floor are realized. According to the invention, the broadband large dynamic DLVA detection sensitivity is obviously improved.

The invention provides a data processing and output circuit and method for a high-energy particle detection pixel sensor chip, and relates to the technical field of high-energy particle detection, and the method comprises the steps that each data processing unit receives ADC output of multiple channels, and carries out the validity discrimination of the received original data of each channel in a polling mode; effective case information in each data processing unit is summarized through a data integration circuit, and the effective case information is cached into a sparse signal output buffer; the data output interface circuit receives the information in the sparse signal output buffer, judges whether effective information needing to be output exists or not, and reads formatted data if the effective information exists. According to the invention, the output signal of the quantization circuit is discriminated so as to distinguish an effective case and a noise background, only pixel data containing an effective particle hit event is subjected to position correlation and coding, pixels not containing the effective hit event are skipped, and whether the data output interface outputs data to the outside is determined according to whether the effective case exists or not.

A system that performs beam selection and beam merging using beam-specific signal quality metrics corresponding to a minimum noise floor. For example, a device may track a minimum noise floor for each beam, determine a highest minimum noise floor across the beams, and determine a noise floor ratio between the beam-specific minimum noise floor and the highest minimum noise floor. Using a combination of the noise floor ratio and signal-to-noise ratio (SNR) values, the device may perform beam selection by prioritizing low background noise as well as high SNR to select a pre-defined beam group. In addition, the device may use the noise floor ratio to perform beam merging and generate single-channel output audio data using the selected beam group. For example, the device may scale the beams based on a combination of the SNR value and the noise floor ratio.

A method of determining a location of a mobile device in the presence of a spoofing signal includes receiving a GNSS signal and detecting a spoofing condition based on a signal strength of the GNSS signal. The spoofing condition may comprise an increase in the signal strength, the signal strength exceeding a threshold value, an increase in a noise floor related to a measurement of the signal strength, or a combination thereof. The method may further comprise, responsive to detecting the spoofing condition, providing an indication of the GNSS signal as a spoofing signal.

The invention relates to the technical field of optical fiber sensing signal processing, in particular to a DAS signal floor noise elimination method, system and equipment based on statistical analysis, and the method comprises the steps: initializing system parameters, and loading a DAS application field data matrix; performing slicing and frequency domain transformation on the DAS original data; dynamically determining a peak threshold value, and performing adaptive peak searching according to the peak threshold value; sorting the identified and screened local maximum values in a descending order, and selecting frequencies corresponding to the local maximum values as main frequency candidates; performing clustering analysis on the selected dominant frequency candidates; calculating the cumulative proportion of each main frequency in the total statistics, selecting a center frequency, and forming the multi-frequency band elimination of the monitoring unit by using band elimination frequency bands formed by different center frequencies; and adding the multi-frequency band elimination to a statistical filtering frequency band set of all the point locations, storing the statistical filtering frequency band set of all the point locations as a statistical model, and performing background noise elimination on the DAS original signal based on the statistical model.

The invention discloses a network signal interference detection system based on spectrum analysis. The system comprises a network signal acquisition module, a spectrum preprocessing module, a spectrum calculation module, a frequency separation spectrum kurtosis analysis module, a Bayesian online change point detection module, a parameter linkage module, an interference discrimination module and a result output module. The frequency separation spectrum kurtosis analysis module outputs kurtosis statistics and noise bottom estimation according to time slices based on the power spectrum; the Bayesian online change point detection module updates and outputs the change point position, the confidence coefficient and the adaptive time window parameter online; the parameter linkage module updates the frequency separation spectrum kurtosis analysis module according to the data; and the interference judgment module generates an interference event list, and the result output module outputs alarm and troubleshooting positioning data. According to the invention, automatic detection and discrimination of same-frequency superposition, adjacent-frequency superposition, burst intermittence and noise bottom uplift interference are realized, the accuracy is improved, and the troubleshooting cost is reduced.

This application discloses a wind tunnel-based noise detection method, apparatus, storage medium, and electronic device. The method includes: when testing a test piece in a wind tunnel, acquiring a measured noise spectrum in the testing environment and obtaining the operating frequency of the wind tunnel's inverter; matching the background noise spectrum corresponding to the operating frequency in a background noise spectrum library based on the operating frequency; aligning and dividing the measured noise spectrum and the background noise spectrum using a frequency band division rule adapted to the inverter's operating frequency to obtain multiple frequency band units; based on the acoustic energy density of the measured noise spectrum and the background noise spectrum within each frequency band unit, extracting the energy component corresponding to the background noise spectrum from the measured noise spectrum through directional orthogonal projection, and reconstructing the spectrum of the residual energy after extraction to obtain the effective aerodynamic noise spectrum of the test piece; and calculating the noise detection result of the test piece based on the effective aerodynamic noise spectrum.

A wireless device is configured to transmit radar signals at extremely low spectral density so that the receiver can be operated at the same time without saturating and thereby enabling full-duplex operation. To achieve sufficient range, beamforming is used together with a very long correlation time for detection of the radar signal, in which case the receiver can resolve backscattered or reflected radar signals far below the noise floor, while the bandwidth of the transmitted radar signal can be very high. The wide bandwidth enables the wireless device to maintain a low power spectral density while increasing output power, and also increase the depth resolution. The extremely low power spectral density even in the transmission beam direction makes the probability that communication will be disturbed very low. The high bandwidth calls for digital filters for both transmitted and received signals to equalize the radar frequency response characteristics of the radio unit.

Systems and methods for a MEMS microphone package are disclosed. The MEMS microphone package may include a first port to direct sound to a MEMS system, including a die substrate, an acoustic membrane, and one or more plates. The MEMS microphone package may include an ASIC to produce microphone output based on an electrical signal, a PCB, a lid, and a second port. The first port and the second port may define a front volume and a back volume respectively. The second port may increase the back volume to improve sensitivity and reduce the acoustic sensor's noise floor to improve signal-to-noise ratio.

[Problem] To provide a determination device, a determination method, a determination program, and a radar device for determining the presence or absence of radio wave interference that does not cause a noise floor rise even when there is radio wave interference and does not require a special pattern provided for radio wave interference. [Solution] A determination device according to one embodiment of the present technology is provided with a reception unit and a determination processing unit. The receiving unit receives a first reflected chirp signal generated by a first transmission chirp signal transmitted toward an object by a transmitting device and reflected from the object, and a second reflected chirp signal generated by a second transmission chirp signal transmitted by the transmitting device at a time different from a time at which the first transmission chirp signal is transmitted and reflected from the object. A determination processing unit compares a plurality of arrival angle spectrums generated based on a plurality of corrected signals obtained by performing a plurality of velocity aliasing correction processes on the first reflected chirp signal and the second reflected chirp signal, and determines whether or not a useless signal is received.

This invention discloses a magnetotelluric denoising method and system based on a physics-driven and conditional diffusion model, belonging to the field of electromagnetic signal processing technology. This method integrates the advantages of neural networks and the physical characteristics of magnetotelluric signals, and proposes a physics-driven synthesis strategy based on statistical priors of measured source data and a curriculum-based dynamic physics-driven noise addition strategy based on training progress factors. Addressing the scarcity of high-quality, clean samples, a clean background field that highly approximates real physical characteristics is synthesized using a parametric mathematical model. The introduction of curriculum-based dynamic physics-driven noise addition (such as noise floor annealing, square wave incrementing, and morphological clustering pulses) accurately simulates natural background degradation and complex human interference, enabling the model to adapt to extremely complex time-frequency characteristics under different geological structural backgrounds. This completely overcomes the domain shift problem caused by static Gaussian white noise training, constructs a richer sample library, and lays the foundation for accurate denoising of conditional diffusion models.

The present disclosure relates to an audio processing method and device, electronic equipment and storage medium, the method comprising: converting an audio signal of a target audio device collected to a frequency domain to obtain first frequency domain information of the audio signal; wherein the first frequency domain information comprises amplitude values of different frequency components of the audio signal; determining a reference value according to the amplitude values; determining noise floor information of the audio signal according to differences between the amplitude values of different frequency components and the reference value; and the noise floor information is used to reflect a noise floor processing capability of the target audio device.

This application discloses a method and product for self-diagnosing external interference. The method includes: acquiring the inherent parameters of the frequency-using terminal to calculate the theoretical noise floor; measuring the total received power in real time and calculating the super-noise power accordingly; when the super-noise power exceeds a preset power threshold, acquiring the baseband demodulation quality parameters of the current channel; and performing correlation discrimination based on the super-noise power and baseband demodulation quality parameters. When the super-noise power is high and the demodulation quality is below a preset threshold, it is determined to be external interference; otherwise, it is determined to be a strong useful signal. This application establishes a two-dimensional causal correlation model between physical layer power and baseband layer quality within the terminal, enabling real-time and objective self-diagnosis of external interference from the terminal's own perspective, thus improving the objectivity, timeliness, and accuracy of the diagnosis.

Background noise estimation and noise reduction are disclosed, in an embodiment, a method includes: obtaining an audio signal; dividing the audio signal into a plurality of buffers; determining time-frequency samples of each buffer of the audio signal; determining, for each buffer and each frequency, a median (or mean) of energies and a measure of energy variation based on samples in the buffer and samples in adjacent buffers, the samples in the buffer and the samples in the adjacent buffers together spanning a specified time range of the audio signal; combining the median (or mean) of energies and the measure of energy variation into a cost function; for each frequency: determining a signal energy of a particular buffer of the audio signal corresponding to a minimum of the cost function; selecting the signal energy as an estimated background noise of the audio signal; and using the estimated background noise to reduce noise in the audio signal.

The invention discloses an optical fiber array coupling system and method based on multi-source data fusion, and relates to the technical field of optical fiber array coupling system.When an automatic coupling machine receives a working instruction, the system firstly starts a pre-coupling process of an optical fiber array, fuses and analyzes multi-source vibration state data, corrects operating parameters in real time, effectively suppresses initial mechanical artifacts, and finally, the optical fiber array is subjected to multi-source data fusion analysis; the adaptability of equipment to external disturbance and signal ground noise is improved, after initialization of operation parameters is completed, the system enters an actual coupling process, key working condition parameters are continuously monitored, and whether further optimization and adjustment are needed or not is dynamically judged, so that the mechanical vibration amplitude and response dispersion in the coupling process are reduced, and the coupling efficiency is improved. According to the optical fiber array coupling detection system, signal bottom noise and mechanical artifact superposition amplification are prevented, the signal quality and the structural stability are improved, when it is detected that an optical fiber array completes coupling, the system executes locking and steady-state processing according to a preset sequence, and the connection anti-interference performance and the long-term stability are improved through technological solidification and closed-loop parameter adjustment.

The invention provides a vehicle-mounted antenna floor noise testing method, device and equipment and a storage medium, a vehicle-mounted antenna is connected to a first port of a three-way connector, a vehicle-mounted receiver is connected to a second port of the three-way connector, and the testing equipment is connected to a third port of the three-way connector. Determining a power spectrum corresponding to the output signal; according to the power frequency spectrum corresponding to the output signal, an equivalent noise broadband corresponding to the vehicle-mounted antenna is determined, and the equivalent noise broadband is used for measuring the anti-interference capability of the vehicle-mounted antenna to environmental noise. As the original access of the vehicle-mounted antenna and the vehicle-mounted receiver does not need to be disconnected, the connector does not need to be repeatedly plugged, the signal acquisition of the vehicle-mounted antenna is realized on the premise of not influencing the normal work of the original vehicle system, and meanwhile, the problem of noise introduction caused by repeatedly plugging the connector is avoided; and the test precision of the floor noise of the vehicle-mounted antenna is improved to a certain extent.

This invention relates to a method for estimating the noise floor of a two-dimensional R-V spectrum based on moving averages. This method is used to process radar echo signals to obtain the noise floor. The radar echo signal is a three-dimensional spectrum, with its three dimensions corresponding to the range index R, the Doppler velocity index V, and the signal amplitude Am under the (R,V) index. The estimation method involves traversing all (R,V) values ​​of the radar echo signal and calculating the noise floor using a two-dimensional moving average method during the traversal. This invention uses a cross-shaped two-dimensional moving average method to estimate the noise floor, increasing the noise floor calculation sample from one-dimensional to two-dimensional. The calculated noise floor is more stable and closer to the true noise floor of the R-V spectrum.

The application discloses a kind of self-adapting transfer function equalization vector sensor background noise measurement method and system, belong to underwater acoustic sensor technical field.The output signal of the vector sensor to be measured and the reference signal of reference sensor are obtained;Based on the reference signal input to adaptive filter, the weight coefficient of adaptive filter is dynamically updated by adaptive algorithm, to establish the time-varying transfer function model from reference sensor to the sensor to be measured;Based on time-varying transfer function model, the reference signal is processed, and the result that input signal is transferred to the sensor to be measured by reference sensor transfer function compensation is obtained;The output signal of the vector sensor to be measured and the compensated signal are input into DHCM algorithm, and the background noise estimation of the sensor to be measured is obtained.The application is used to solve the problem that indirect measurement algorithm for different sensors will be distorted, aims at effectively improving the accuracy and environmental adaptability of low-frequency band background noise measurement.

This invention belongs to the field of noise floor reduction and proposes a frequency-band noise floor reduction method based on a spectrum analyzer. This invention can solve the problems of uneven noise power distribution and excessive noise power estimation. It includes the following steps: acquiring the noise floor signal of the spectrum analyzer, wherein the noise floor signal is a low-pass or band-limited white noise signal with a specified bandwidth; dividing the specified bandwidth into a first number of sub-segments and dividing the signal power before denoising into a first number of sub-segments; estimating the noise power under the specified bandwidth and dividing it into a first number of sub-segments; denoising each sub-segment and calculating the noise power spectral density of each sub-segment after denoising based on the bandwidth after segmentation, the signal power before denoising after segmentation, and the estimated noise power after segmentation; and ensuring that the noise power spectral density after denoising is not negative by using the compression factor method or the square root method.

The application relates to a dual cascade flow type processing method and system for removing artifacts from myoelectric signals and reconstructing the signals, and relates to the technical field of biomedical signal processing.The method comprises the following steps: extracting a standard stimulation artifact template from a noisy calibration myoelectric signal, calculating a decision threshold and a frequency domain noise floor baseline; transversely splicing a myoelectric signal data block with historical data in a time domain residual artifact cache, and performing artifact identification and cancellation based on the standard stimulation artifact template and a morphological gating mechanism; sending a time domain processing signal into a frequency domain splicing cache, performing fast Fourier transform after reaching a preset window length; dynamically generating a probability mask according to the frequency domain noise floor baseline, performing inverse fast Fourier transform, and obtaining a single-frame recovered signal; and obtaining a smooth and continuous reconstructed myoelectric signal by using an overlap-add method; and on the premise of not depending on high-performance hardware, accurate cancellation of stimulation artifacts is realized, and real physiological signals are protected.

The application provides a howling suppression method, a sound pickup terminal and a howling suppression system, which are applied to the sound pickup terminal. The method comprises the following steps: monitoring the distance between the sound pickup terminal and a collection terminal in real time; if the distance is less than a preset distance threshold, performing howling suppression according to a first howling suppression strategy; if the distance is greater than or equal to the preset distance threshold, performing noise floor detection and howling detection to obtain a noise floor detection result and a howling detection result, performing howling suppression according to a second howling suppression strategy according to the noise floor detection result, and / or performing howling suppression according to a third howling suppression strategy according to the howling detection result, so as to improve the howling suppression effect and reduce the timbre distortion degree.

The invention discloses a fast deconvolution sound source localization method based on array sparsity and broadband comprehensive processing, which comprises the following steps of: selecting microphones to form a sparse array, and converting a regular focusing plane into an irregular plane to obtain grid coordinates; calculating a sparse array cross-spectrum matrix based on coordinates, obtaining a function beam through eigenvalue decomposition and power exponent operation, and outputting the function beam; constructing a frequency domain deconvolution model according to the convolution relationship between the frequency domain and sound source distribution and the convolution relationship between the frequency domain and a rising power point spread function, and solving a sound source intensity distribution matrix by using fast Fourier transform and iterative optimization; estimating a noise floor-to-signal-to-noise ratio matrix, traversing frequency points, taking the maximum signal-to-noise ratio, and fusing the frequency points into a broadband imaging matrix; a time smoothing factor is introduced to carry out weighted fusion on the inter-frame imaging matrix, and positioning is completed; according to the method, low-consumption, high-precision and strong-robustness sound source localization is realized through array rarefaction and fast deconvolution load reduction, precision protection by means of function beam forming and Gaussian filtering, multi-frequency point fusion adaptive imaging, inter-frame smooth flicker suppression and adaptation to complex scenes.

The invention discloses an audio system ground noise reduction circuit and method, and the circuit comprises a voltage follower which is disposed between an audio chip and a power amplifier chip, receives an analog signal outputted by the audio chip, and outputs a forward analog signal with the same amplitude as the analog signal; the signal turner is arranged between the audio chip and the power amplifier chip and is used for receiving the analog signal output by the audio chip and outputting a reverse analog signal with the same amplitude as the analog signal; the analog switch is connected in series with the signal turner and then connected in parallel with the voltage follower; the MPU processor is connected with the analog switch, when the audio system is powered on or receives a sound playing stopping instruction, the analog switch is controlled to be kept closed, and the forward analog signal and the reverse analog signal are output to the power amplifier chip after being superposed; and when a sound playing instruction is received, the analog switch is controlled to be turned on, and a forward analog signal is output to the power amplifier chip. According to the invention, the background noise of the power amplifier audio system, especially an AB type power amplifier audio system, can be effectively reduced.

The invention discloses a vector sensor background noise measurement method and system based on adaptive transfer function equalization, and belongs to the technical field of underwater acoustic sensors. Acquiring an output signal of the to-be-measured vector sensor and a reference signal of the reference sensor; inputting a reference signal into an adaptive filter, and dynamically updating a weight coefficient of the adaptive filter through an adaptive algorithm to establish a time-varying transfer function model from the reference sensor to the sensor to be tested; processing the reference signal based on a time-varying transfer function model, and obtaining a result of compensation of the input signal to the transfer function of the to-be-tested sensor through the reference sensor transfer function; and inputting the output signal of the vector sensor to be measured and the compensated signal into a DHCM algorithm to obtain background noise estimation of the sensor to be measured. The method is used for solving the problem of distortion of an indirect measurement algorithm for different sensors, and aims to effectively improve the accuracy and environmental adaptability of low-frequency-band background noise measurement.

The application relates to the technical field of optical fiber sensing signal processing, in particular to a DAS signal bottom noise elimination method, system and device based on statistical analysis, which comprises the following steps: initializing system parameters, and loading a DAS application field data matrix; slicing and performing frequency domain transformation on DAS original data; dynamically determining a peak threshold value, and adaptively searching for a peak according to the peak threshold value; performing descending order sorting on local maximum values screened out through identification, and selecting a frequency corresponding to the local maximum values as a main frequency candidate; performing clustering analysis on the selected main frequency candidate; calculating the cumulative proportion of each main frequency in total statistics, selecting a center frequency, forming a band-stop frequency band by different center frequencies, and composing a multi-frequency band-stop of the monitoring unit; adding the multi-frequency band-stop to a statistical filtering frequency band set of all point positions, saving the statistical filtering frequency band set of all point positions as a statistical model, and eliminating bottom noise of a DAS original signal based on the statistical model.

This invention provides a method for processing echo signals from a fish finder, the fish finder itself, and a storage medium, comprising: S1, receiving an echo signal; S2, performing envelope processing on the echo signal to obtain an envelope signal; S3, calculating the variance of data within a selected sliding window until a signal segment with a variance value less than a predetermined variance threshold is found; S4, determining the noise floor value of the envelope signal based on the average amplitude of the found signal segment; S5, determining the starting position of a weak signal data segment based on the location of signal points with amplitudes less than the noise floor value; S6, performing partial filtering on the envelope signal to obtain the image signal to be displayed, and performing IIR filtering or FIR filtering on the weak signal data segment; signal segments other than the weak signal data segment retain their original values. Using the above technical solution, weak signals in the echo signal can be identified, thereby improving the sensitivity of the fish finder.

The application relates to the technical field of low-power electronic circuit control, and discloses a low-power circuit control system based on motion amplitude change, which comprises a motion sensor sub-module, a noise feature acquisition sub-circuit, a low-power control unit, a main function module and a power management module. The motion sensor sub-module is used for outputting only an analog signal containing a background noise component without performing normal motion measurement in a low-power bias mode. The noise feature acquisition sub-circuit is used for obtaining a current feature vector composed of a noise envelope average value and a noise envelope variance. The low-power control unit is used for switching the system among a noise monitoring standby state, a noise feature verification state and a main function working state. The main function module is used for starting and executing a predetermined function task when the main function working state is entered. The background noise component of the motion sensor is used as a judgment basis in the low-power bias mode, the structure reduces the number of function circuits of the motion sensor participating in the standby stage, and thus the energy consumption of the system in the standby stage is reduced.

The invention discloses a code hopping spread spectrum communication timing synchronization method and system, and the method employs a signal detection and frame synchronization method to carry out timing coarse synchronization, can calculate the noise floor in real time according to the change of an electromagnetic environment, takes the ratio of a useful signal incoherent processing result to a noise signal incoherent processing result as a detection quantity, is not sensitive to the change of the electromagnetic environment, and is high in detection precision. The risk of signal false capture or missing capture can be effectively avoided; a timing fine synchronization method is adopted, sampling errors are estimated through a correlation peak fitting mode, real-time correction of the sampling errors is achieved through an online table look-up and time domain linear filtering method, the method has the advantages of being high in estimation precision and good in real-time performance under the low signal-to-noise ratio, meanwhile, sampling error estimation and correction are conducted in the time domain, hardware resource overhead is effectively reduced, and the method is suitable for popularization and application. The antenna can meet the requirements of long-distance and high-reliability broadband communication under low power, and can be applied to the working scenes of short-time burst communication and continuous wave communication.

This invention provides a method, system, storage medium, and electronic device for generating random nuclear-simulated pulses. The system is triggered by a button, and the feedback coefficient and reading rules are derived from the trigger time. A linear feedback register module generates a random trigger signal, controlling the pulse shape ROM to output discrete pulse shape data. Simultaneously, a ring oscillation module receives the trigger signal and outputs a random address with random jitter as the entropy source, which is input to an energy standard library controlled by a serial port host module and a noise floor ROM, outputting the pulse energy value. The pulse shape data is multiplied by the energy value and then added to the output of a Gaussian random number ROM to obtain nuclear-simulated pulse data with random intervals, random energy, satisfying the input nuclide ratio, and containing noise floor. Finally, the nuclear-simulated pulse is obtained through analog-to-digital conversion. The system implementation method of this invention is simple, reliable, and highly controllable. Based on a high degree of randomness, the energy ratio of the nuclear-simulated pulse can be flexibly adjusted to meet the simulation requirements of various high-energy environments.